Techno-Economic Optimization and Assessment of Solar Photovoltaic–Battery–Hydrogen Energy Systems with Solar Tracking for Powering ICT Facility
Bibliographic record
Abstract
This paper addresses the critical issue of selecting the optimal solar tracking configuration for maximum energy generation, given the increasing demand for sustainable energy solutions in information and communication technology (ICT) facilities. The main goal is to thoroughly evaluate and compare seven different solar tracking configurations across technical, economic, and environmental dimensions: No Tracking (NT), Monthly Adjusted Horizontal Axis (MAHA), Weekly Adjusted Horizontal Axis (WAHA), Daily Adjusted Horizontal Axis (DAHA), Continuously Adjusted Horizontal Axis (CAHA), Continuously Adjusted Vertical Axis (CAVA), and Dual Axis with Continuous Adjustment (DACA). This study utilizes the HOMER simulation program to evaluate its energy and hydrogen production, emissions, and cost-effectiveness performance. Key findings indicate solar tracking improves energy efficiency, with optimal capacity factors of 18.2% and 17.7% for CAHA and DAHA configurations, respectively. Although load-following strategies increase reliability, there is a trade-off between capital costs and energy costs. In addition, an MCDM approach helps to consolidate the evaluation, resulting in CAVA being ranked as the most preferable option. The study contributes to informed decision-making for energy systems in ICT facilities by emphasizing the significance of considering a variety of criteria and evaluation techniques to address complex energy challenges.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".